首选标签云可视化格式 [英] Preferable Tag Cloud Visualization Formats

问题描述

出于好奇，我很想知道哪种标签云格式最能满足发现越来越多(相关)内容的目的?

Out of curiosity, I would love to know what tag clouds formats best serve the purpose of discovery of more and more (relevant)content?

我知道3种格式，但是不知道哪种格式最好.

I am aware of 3 formats, but don't know which one is the best.

1)可口的一个-颜色底纹

2)标准字体，其字体大小有所不同-

2) The standard one with font size variations -

3)此网站上的一个-显示重要性/用途的数字.

3) The one on this site - numbers showing importance/usage.

那么您更喜欢哪个呢?为什么?

So which ones do you prefer? and why?

修改: 多亏了以下答案，我现在对标签云的可视化技术有了更多的了解.

Thanks to the answers below, I now have much more understanding of tag cloud visualization techniques.

4)并行标记云-简单易用坐标技术.我发现它更具组织性和可读性.

4) Parallel Tag Clouds - a simple use of parallel coordinates technique. I find it more organized and readable.

5) voroni图-有助于识别标记关系并基于它们做出决策.不符合我们发现相关内容的目的.

5) voroni diagram - more useful for identifying tag relationships and making decisions based on them. Doesn't serves our purpose of discovery of relevant content.

6)思维导图-它们很好，可用于逐步过滤内容.

6) Mind maps - They are good and can be employed to step by step filter content.

我在这里发现了一些更有趣的技术- http://www.cs .toronto.edu/〜ccollins/research/index.html

I found some more interesting techniques here - http://www.cs.toronto.edu/~ccollins/research/index.html

推荐答案

我确实认为这取决于信息的内容和受众.与一个人有关的东西与另一个人无关的东西.如果受众更加专业，那么他们将更有可能按照相同的思路思考，但是内容提供商仍需要对其进行分析和迎合.

I really do think that depends on the content of the information and the audience. What's relevant to one is not relevant to another. If an audience is more specialized, then they will be more likely to think along the same lines, but it would still need to be analyzed and catered to by the content provider.

一个人可以采取多种途径发现更多".以标签"DNS"为例.您可以向下钻取更具体的详细信息，例如"UDP端口53"和"MX记录"，也可以使用诸如"IP地址"，主机名"和"URL"之类的术语. Voronoi 图显示了群集，但无法处理一般情况术语可能与许多概念有关.主机名映射到"DNS"，"HTTP"，"SSH"等.

There are also multiple paths that a person can take to "discover more". Take the tag "DNS" for example. You could drill down to more specific details like "UDP Port 53" and "MX Record", or you could go sideways with terms like "IP address" "Hostname" and "URL". A Voronoi diagram shows clusters, but wouldn't handle the case where general terms could be related to many concepts. Hostname mapping to "DNS", "HTTP", "SSH" etc.

我注意到，在某些标签云中，通常有一个或两个项目比其他项目大得多.思维导图可以解决这些问题，其中一个中心概念会辐射出其他概念.

I've noticed that in certain tag clouds there's usually one or two items that are vastly larger than the others. Those sorts of things could be served by a mind map, where one central concept has others radiating out from it.

对于思维导图不合适的许多主要主题"，有平行坐标，但这会让许多净用户感到困惑.

For the cases of lots of "main topics" where a mind map is inappropriate, there are parallel coordinates but that would be baffling to many net users.

我认为，如果我们找到一种对标签簇进行排序的非常有条理的方法，同时又保留了通用性和特异性之间的联系，那将对AI研究有所帮助.

I think that if we found an extremely well organized way of sorting clusters of tags while preserving links between generalities and specificities, that would be somewhat helpful to AI research.

就我个人而言，我认为数字方法很好，因为不经常引用的标签仍以可读的字体大小显示.我还认为SO之所以这样做是因为它们比标准的基于平均大小的云覆盖的标签要多得多.

In terms of which I personally prefer, I think the numeric approach is nice because infrequently referenced tags are still presented at a readable font size. I also think SO does it this way because they have vastly more tags to cover than the average size based cloud a la the standard.

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